Filter-Based Fault Diagnosis and Remaining Useful Prediction

This book unifies existing and emerging concepts concerning state estimation, fault detection, fault isolation and fault estimation on industrial systems with an emphasis on a variety of network-induced phenomena, fault diagnosis and remaining useful life for industrial equipment. It covers state estimation/monitor, fault diagnosis and remaining useful life prediction by drawing on the conventional theories of systems science, signal processing and machine learning.

This book unifies existing and emerging concepts concerning state estimation, fault detection, fault isolation and fault estimation on industrial systems with an emphasis on a variety of network-induced phenomena, fault diagnosis and remaining useful life for industrial equipment. It covers state estimation/monitor, fault diagnosis and remaining useful life prediction by drawing on the conventional theories of systems science, signal processing and machine learning.

Features:

• Unifies existing and emerging concepts concerning robust filtering and fault diagnosis with an emphasis on a variety of network-induced complexities.
• Explains theories, techniques, and applications of state estimation as well as fault diagnosis from an engineering-oriented perspective.
• Provides a series of latest results in robust/stochastic filtering, multidate sample, and time-varying system.
• Captures diagnosis (fault detection, fault isolation and fault estimation) for time-varying multi-rate systems.
• Includes simulation examples in each chapter to reflect the engineering practice.

This book aims at graduate students, professionals and researchers in control science and application, system analysis, artificial intelligence, and fault diagnosis.